GitHub - sourabhrajwade/Machine-Learning-Coursera: This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

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This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (…

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machine-learning-ex1		machine-learning-ex1
machine-learning-ex2		machine-learning-ex2
machine-learning-ex3		machine-learning-ex3
machine-learning-ex4		machine-learning-ex4
machine-learning-ex5		machine-learning-ex5
machine-learning-ex6		machine-learning-ex6
machine-learning-ex7		machine-learning-ex7
machine-learning-ex8		machine-learning-ex8
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Lecture1.pdf		Lecture1.pdf
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